def test_resnet3d_18(resnet3d_test):
"""Test 18."""
K.set_image_data_format('channels_last')
model = Resnet3DBuilder.build_resnet_18((224, 224, 224, 1), 2)
resnet3d_test(model)
K.set_image_data_format('channels_first')
model = Resnet3DBuilder.build_resnet_18((1, 512, 512, 256), 2)
resnet3d_test(model)
def test_resnet3d_18():
K.set_image_data_format('channels_last')
model = Resnet3DBuilder.build_resnet_18((224, 224, 224, 1), 2)
model.compile(loss="categorical_crossentropy", optimizer="sgd")
assert True, "Failed to build with tensorflow"
K.set_image_data_format('channels_first')
model = Resnet3DBuilder.build_resnet_18((1, 512, 512, 256), 2)
model.compile(loss="categorical_crossentropy", optimizer="sgd")
assert True, "Failed to build with theano"
def create_reg_resnet18_3D(img_x, img_y, ch_n, seq_len, tgt_size):
resnet18_3D = Resnet3DBuilder.build_resnet_18(
(seq_len, img_x, img_y, ch_n), tgt_size)
resnet18_3D.layers.pop()
layer = Dense(32, activation='relu')(resnet18_3D.output)
resnet18_3D.layers[-1].outbound_nodes = []
resnet18_3D.outputs = [resnet18_3D.layers[-1].output]
output = resnet18_3D.get_layer('flatten_1').output
output = Dense(32, activation='relu')(output)
output = Dense(1, activation='linear')(output)
reg_resnet18_3D = Model(resnet18_3D.input, output)
return reg_resnet18_3D
**init_args['volume_image_data_generator']['val']['init'])
train_vol_loader = NPYDataLoader(
**init_args['volume_image_data_loader']['train'])
val_vol_loader = NPYDataLoader(**init_args['volume_image_data_loader']['val'])
train_iter_args = init_args['volume_image_data_generator']['train'][
'flow_from_loader']
train_iter_args['volume_image_data_loader'] = train_vol_loader
val_iter_args = init_args['volume_image_data_generator']['val'][
'flow_from_loader']
val_iter_args['volume_image_data_loader'] = val_vol_loader
image_shape = train_datagen.image_shape
regularization_factor = 1
model = Resnet3DBuilder.build_resnet_18(image_shape, nb_classes,
regularization_factor)
model.load_weights(weights)
compile_args = init_args['model']['compile']
compile_args['optimizer'] = Adam(lr=1e-4)
model.compile(**compile_args)
model_fit_args = init_args['model']['fit_generator']
model_fit_args['generator'] = train_datagen.flow_from_loader(**train_iter_args)
model_fit_args['validation_data'] = val_datagen.flow_from_loader(
**val_iter_args)
model_fit_args['callbacks'] = [
checkpointer, lr_reducer, early_stopper, csv_logger
]
model.fit_generator(**model_fit_args)
model.save('output/resnet18_{}_ctd.h5'.format(title))
print("Valid hdf5 file in 'read' mode: " + str(hdf5_file))
file_size = os.path.getsize(path_to_hdf5)
print('Size of hdf5 file: {:.3f} GB'.format(file_size/2.0**30))
num_rows = hdf5_file['input'].shape[0]
print("There are {} images in the dataset.".format(num_rows))
print("The datasets within the HDF5 file are:\n {}".format(list(hdf5_file.values())))
input_shape = tuple(list(hdf5_file["input"].attrs["lshape"]))
batch_size = args.batchsize # Batch size to use
print ("Input shape of tensor = {}".format(input_shape))
from resnet3d import Resnet3DBuilder
model = Resnet3DBuilder.build_resnet_18((64, 64, 64, 1), 1) # (input tensor shape, number of outputs)
tb_log = keras.callbacks.TensorBoard(log_dir=TB_LOG_DIR,
histogram_freq=0,
batch_size=batch_size,
write_graph=True,
write_grads=True,
write_images=True,
embeddings_freq=0,
embeddings_layer_names=None,
embeddings_metadata=None)
checkpointer = keras.callbacks.ModelCheckpoint(filepath=CHECKPOINT_FILENAME,
monitor="val_loss",
verbose=1,
import numpy as np from resnet3d import Resnet3DBuilder # pseudo volumetric data X_train = np.random.rand(10, 64, 64, 32, 1) labels = np.random.randint(0, 2, size=[10]) y_train = np.eye(2)[labels] # train model = Resnet3DBuilder.build_resnet_18((64, 64, 32, 1), 2) model.compile(loss="categorical_crossentropy", optimizer="sgd") model.fit(X_train, y_train, batch_size=10)
